Estimating Derivatives in Nonseparable Models with Limited Dependent Variables

Joseph Altonji, Hidehiko Ichimura and Taisuke Otsu
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Taisuke Otsu: Cowles Foundation, Yale University, https://cowles.yale.edu/

No 1668, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables. We treat models in which Y is censored from above or below or potentially from both. The basic idea is to first estimate the derivative of the conditional mean of Y given X at x with respect to x on the uncensored sample without correcting for the effect of changes in x induced on the censored population. We then correct the derivative for the effects of the selection bias. We propose nonparametric and semiparametric estimators for the derivative. As extensions, we discuss the cases of discrete regressors, measurement error in dependent variables, and endogenous regressors in a cross section and panel data context.

Keywords: Censored regression; Nonseparable models; Endogenous regressors; Tobit; Extreme quantiles (search for similar items in EconPapers)
JEL-codes: C1 C14 C23 C24 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2008-07
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (9)

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Related works:
Journal Article: Estimating Derivatives in Nonseparable Models With Limited Dependent Variables (2012)
Working Paper: Estimating Derivatives in Nonseparable Models with Limited Dependent Variables (2011)
Working Paper: Estimating derivatives in nonseparable models with limited dependent variables (2008)
Working Paper: Estimating Derivatives in Nonseparable Models with Limited Dependent Variables (2008)
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